In a nutshell, advanced analytics represents a collection of techniques used to model internal and external data to yield valuable insights that can drive business-improving actions.
We call this collection of techniques “advanced” analytics to differentiate them from the traditional analytics approach normally accomplished using business intelligence (BI) systems.
To help you understand the differences between traditional BI and advanced analytics, I want to elaborate on four main ideas.
Difference 1: Their Purpose
In traditional BI, data is processed to inform business users of the past performance of business operations. Thus, the data is gathered and aggregated into a clean format for reporting purposes.
Nowadays, the expectation of traditional BI systems is extending into a new area, analysis-oriented BI. Organizations want to get a 360-degree view of their customers in a timely manner, identify the root causes of success or failure in business operations, and control as much future uncertainty as possible. These demands can’t be satisfied with traditional BI dashboards or reports and can’t be supported by traditional BI architecture.
This is where advanced analytics comes in to solve complex business problems. In this way, advanced analytics serves as a trouble-shooting player in an organization rather than information provider.
Difference 2: The Approach
There is a fundamental difference between traditional and advanced analytics, namely the process followed to design and solve a business problem. In traditional BI, the analysis is typically built to be repeatable. IT develops the reporting template and extracts certain information important to the business in assessing historical performance. Thus, the types of information analyzed and the format in which the information is presented is predefined.
The advanced analytics techniques that have become more mainstream remind people there is another approach: a question or doubt is raised first, then a set of analysis is designed to dive into the data and mine the business insight to answer the question. In this approach, IT typically only provides the analytical platform. The business then directly collects what it wants.
Advanced analytics software vendors provide a friendly user interface to allow people with varied backgrounds to utilize the data to find the answer to their questions. Often the software will guide the user through the techniques by helping select and process the relevant information from multiple resources.
Difference 3: The Data Used
Data used for analysis in traditional BI is typically gathered from a data warehouse or a data mart. The BI platform connects to the data sources, queries and aggregates data to higher levels in a hierarchy, such as geography, timeframe or category. Business users review the aggregated reports to understand historical operational performance. If any unusual observations are identified, they can drill down to lower/related levels for more detail.
Characterizing traditional analysis as starting from large to small, advanced analytics starts from small. With the statistical methods and enhanced computational power, now we are able to capture the unique characteristics of each individual customers instead of analyzing customers at a segment level. So it becomes possible to make personalized marketing activities and improve marketing ROI.
Due to the use of data lakes and various data processing tools, unstructured data such as social media comments, images, and videos become valuable sources of information. Stream computing, furthermore, helps people access and process real-time information. Organizations can monitor market sentiment and brand engagement to measure effectiveness of a marketing campaign. They can also improve the product design by analyzing social comments. Thus, more complete, timely and various data are analyzed with advanced analytics.
Difference 4: The Techniques
When we say advanced analytics, “advanced” refers to quantitative methods such as statistics, algorithms and stochastic processes. Although not all of the advanced analytics techniques are predictive, they are future-oriented since the key idea of the methods is to support data-driven decisions in the future. The advanced analytics techniques can be categorized into three functions:
Descriptive analysis aims to understand an underlying phenomenon or process. The analysis will answer questions like, “What are the typical characteristics of customers who tend to churn?”, or, “Which products do consumers usually purchase together?”
Similar to descriptive analytics, data science involves sifting through data to discover unknown patterns and correlations. It differs in that it tends to be an exploratory process with a loosely defined scope. The goal is to come away with questions that you would try to answer with further analysis.
Predictive analysis studies the hidden relationships between factors and outcomes and then forecasts or estimates an unknown value. For example, a predictive model will allow us to predict which customers are going to churn, or estimate how much revenue will be lost if temperatures drop 10 degrees.
Simulation and Optimization
Simulation imitates the operation and characteristics of a process and summarizes the outcome. Optimization prioritizes the decision options based on a key performance index. For example, if we want to design a drive-through route for a restaurant, we can simulate the traffic and ordering process, compare the simulation outputs for several options, optimize the design and select the best choice.